Automatic camera calibration and geo-registration using objects that provide positional information

ABSTRACT

A video sequence of a field of view within an environment is received. Targets are detected in the video sequence. Target geo-positional information is received. Correspondences between the targets detected in the video sequence and the target geo-positional information are determined and used to calibrate the camera and to geo-register a field of view of the camera.

CROSS-REFERENCE TO RELATED PATENTS AND PATENT PUBLICATIONS

The following patent and patent publications, the subject matters ofwhich are entirely incorporated herein by reference, are mentioned:

U.S. Pat. No. 6,999,600, entitled “Video Scene Background MaintenanceUsing Change Detection and Classification,” by Venetianer, et al.,issued Feb. 14, 2006;

U.S. Published Patent Application No. 2005/0146605, entitled “VideoSurveillance System Employing Video Primitives,” by Lipton et al.,published Jul. 7, 2005;

U.S. Published Patent Application No. 2005/0162515, entitled “VideoSurveillance System,” by Venetianer et al., published Jul. 28, 2005; and

U.S. Published Patent Application No. 2006/0268111, entitled“Multi-State Target Tracking,” by Zhang et al., published Nov. 30, 2006.

FIELD OF THE INVENTION

The present invention relates to automatic camera calibration andgeo-registration that may be used with video cameras and systems for,for example, video surveillance and/or monitoring.

BACKGROUND

Image-based systems are commonly used in different businesses andindustries. These systems are composed of a video camera that obtainsand records images within its sensory field. For example, a video cameraprovides a video record of whatever is within the field of view (FOV) ofits lens. The obtained images may then be monitored by a human operatorand/or reviewed later by a human operator. Recent progress has allowedsuch images to be monitored also by an automated system, improvingperformance and saving human labor.

Effective automatic analysis of the images requires knowledge of thevideo camera's intrinsic parameters, its geometry and relationship withother sensors, geographical location, and/or relationship of actualmeasurement of objects in the camera's field of view with other imagecounterparts. Current systems use error-prone manual sensor calibrationprocesses to obtain such information, and are time consuming and laborintensive. In addition, maintaining calibration between sensors in anetwork is a daunting task, since a slight change in the position of asensor requires the calibration process to be repeated. Further, thecurrently available auto-calibration techniques use image-basedinformation solely and, hence, are limited in their application andreliability. In addition, these techniques are unable to performcalibration or geo-registration without manual intervention.

SUMMARY

One exemplary embodiment of the invention includes a computer-readablemedium comprising software for video processing, which when executed bya computer system, cause the computer system to perform operationscomprising a method of: receiving a video sequence of a field of viewwithin an environment; detecting targets in the video sequence;receiving target geo-positional information transmitted by ageo-positional sensor; and determining correspondences between thetargets detected in the video sequence and the target geo-positionalinformation.

Another exemplary embodiment of the invention includes a computer-basedsystem to perform a method for video processing, the method comprising:a computer-based system to perform a method for video processing, themethod comprising: receiving a video sequence of a field of view withinan environment; detecting targets in the video sequence; receivingtarget geo-positional information transmitted by a geo-positionalsensor; and determining correspondences between the targets detected inthe video sequence and the target geo-positional information.

Another exemplary embodiment of the invention includes a method forvideo processing comprising: receiving a video sequence of a field ofview within an environment; detecting targets in the video sequence;receiving target geo-positional information transmitted by ageo-positional sensor; and determining correspondences between thetargets detected in the video sequence and the target geo-positionalinformation.

Another exemplary embodiment of the invention includes a system toperform video processing of a video sequence generated by at least onevideo camera having a field of view within an environment, comprising atleast one of: a correspondence module to determine correspondencesbetween targets detected in the video sequence and corresponding targetgeo-positional information transmitted by a geo-positional sensor; acalibration and geo-registration module to determine a relationshipbetween the video camera and geo-positional sensor and geo-register thevideo camera based at least on the determined correspondences; or ametric rules and video understanding module to obtain measurementinformation regarding at least one of the environment or target detectedin the video sequence.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features of various embodiments will be apparentfrom the following, more particular description of such embodiments, asillustrated in the accompanying drawings, wherein like reference numbersgenerally indicate identical, functionally similar, and/or structurallysimilar elements.

FIG. 1 illustrates an exemplary video processing system;

FIG. 2A illustrates an exemplary flowchart;

FIG. 2B illustrates an exemplary visibility map;

FIG. 3 illustrates another exemplary flowchart;

FIG. 4 illustrates an exemplary port surveillance scenario;

FIG. 5 illustrates another exemplary video processing system;

FIG. 6 illustrates another exemplary video processing system;

FIG. 7 illustrates another exemplary video processing system; and

FIG. 8 illustrates another exemplary system according to an exemplaryvideo processing system.

DEFINITIONS

In describing the invention, the following definitions are applicablethroughout (including above).

“Video” may refer to motion pictures represented in analog and/ordigital form. Examples of video may include: television; a movie; animage sequence from a video camera or other observer; an image sequencefrom a live feed; a computer-generated image sequence; an image sequencefrom a computer graphics engine; an image sequences from a storagedevice, such as a computer-readable medium, a digital video disk (DVD),or a high-definition disk (HDD); an image sequence from an IEEE1394-based interface; an image sequence from a video digitizer; or animage sequence from a network.

A “video sequence” refers to some or all of a video.

A “video camera” may refer to an apparatus for visual recording.Examples of a video camera may include one or more of the following: avideo camera; a digital video camera; a color camera; a monochromecamera; a camera; a camcorder; a PC camera; a webcam; an infrared (IR)video camera; a low-light video camera; a thermal video camera; aclosed-circuit television (CCTV) camera; a pan, tilt, zoom (PTZ) camera;and a video sensing device. A video camera may be positioned to performsurveillance of an area of interest.

“Video processing” may refer to any manipulation and/or analysis ofvideo, including, for example, compression, editing, surveillance,and/or verification.

A “frame” may refer to a particular image or other discrete unit withina video.

An “Automatic Identification System (AIS) transmitter” may refer to adevice that transmits positional data and identifying informationregarding its carrier. The standard is defined by the InternationalMaritime Organization and regulated by Safety of Life at Sea Conventions(SOLAS) amendment from Dec. 13, 2002, Chapter V, Regulation 19.2 (ISBN928014183X).

A “Blue Force transponder” may refer to a device that transmitspositional and identifying information about its carrier. The Blue Forcetransponder may transmit periodically or when requested.

A “computer” may refer to one or more apparatus and/or one or moresystems that are capable of accepting a structured input, processing thestructured input according to prescribed rules, and producing results ofthe processing as output. Examples of a computer may include: acomputer; a stationary and/or portable computer; a computer having asingle processor, multiple processors, or multi-core processors, whichmay operate in parallel and/or not in parallel; a general purposecomputer; a supercomputer; a mainframe; a super mini-computer; amini-computer; a workstation; a micro-computer; a server; a client; aninteractive television; a web appliance; a telecommunications devicewith internet access; a hybrid combination of a computer and aninteractive television; a portable computer; a tablet personal computer(PC); a personal digital assistant (PDA); a portable telephone;application-specific hardware to emulate a computer and/or software,such as, for example, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application specific integratedcircuit (ASIC), an application specific instruction-set processor(ASIP), a chip, chips, or a chip set; a system on a chip (SoC), or amultiprocessor system-on-chip (MPSoC); an optical computer; a quantumcomputer; a biological computer; and an apparatus that may accept data,may process data in accordance with one or more stored softwareprograms, may generate results, and typically may include input, output,storage, arithmetic, logic, and control units.

“Software” may refer to prescribed rules to operate a computer. Examplesof software may include: software; code segments; instructions; applets;pre-compiled code; compiled code; interpreted code; computer programs;and programmed logic.

A “computer-readable medium” may refer to any storage device used forstoring data accessible by a computer. Examples of a computer-readablemedium may include: a magnetic hard disk; a floppy disk; an opticaldisk, such as a CD-ROM and a DVD; a magnetic tape; a flash removablememory; a memory chip; and/or other types of media that can storemachine-readable instructions thereon.

A “computer system” may refer to a system having one or more computers,where each computer may include a computer-readable medium embodyingsoftware to operate the computer. Examples of a computer system mayinclude: a distributed computer system for processing information viacomputer systems linked by a network; two or more computer systemsconnected together via a network for transmitting and/or receivinginformation between the computer systems; and one or more apparatusesand/or one or more systems that may accept data, may process data inaccordance with one or more stored software programs, may generateresults, and typically may include input, output, storage, arithmetic,logic, and control units.

A “network” may refer to a number of computers and associated devicesthat may be connected by communication facilities. A network may involvepermanent connections such as cables or temporary connections such asthose made through telephone or other communication links. A network mayfurther include hard-wired connections (e.g., coaxial cable, twistedpair, optical fiber, waveguides, etc.) and/or wireless connections(e.g., radio frequency waveforms, free-space optical waveforms, acousticwaveforms, etc.). Examples of a network may include: an internet, suchas the Internet; an intranet; a local area network (LAN); a wide areanetwork (WAN); and a combination of networks, such as an internet and anintranet. Exemplary networks may operate with any of a number ofprotocols, such as Internet protocol (IP), asynchronous transfer mode(ATM), and/or synchronous optical network (SONET), user datagramprotocol (UDP), IEEE 802.x, etc.

DETAILED DESCRIPTION

Exemplary embodiments are discussed in detail below. While specificexemplary embodiments are discussed, it should be understood that thisis done for illustration purposes only. In describing and illustratingthe exemplary embodiments, specific terminology is employed for the sakeof clarity. However, the described is not intended to be limited to thespecific terminology so selected. A person skilled in the relevant artwill recognize that other components and configurations may be usedwithout parting from the spirit and scope of the described. It is to beunderstood that each specific element includes all technical equivalentsthat operate in a similar manner to accomplish a similar purpose. Eachreference cited herein is incorporated by reference. The examples andembodiments described herein are non-limiting examples.

According to the exemplary embodiments, calibration and geo-registrationparameters may be automatically obtained, maintained, and updated forone or more video cameras. The conventional approaches ignore the delugeof positional calibration information available today, which areprovided by objects, such as, for example: cell phones; radio frequencyidentification (RFID) tags on or in, for example, trucks, forklifts,pallets, containers, products, and goods; automated identificationsystem (AIS) transponders in ships; aircraft transponders; blue forcetransponders; and vehicles equipped with global positioning system(GPS), such as, for example, buses, taxis, cars, trucks, railroad carsand others. The ubiquity and abundance of this information may providereliable and accurate auto-calibration and geo-registration for videocameras, even in environments where conventional image-based systems mayfail to perform, such as in feature-less environments. The exploitationof the supplemental source of information may also enable the system toautomatically handle changes in its geometry and performgeo-registration without requiring manual labor.

The following is directed to the automatic calibration andgeo-registration of one or more video cameras by using the positionalinformation provided by the objects in the environment. The calibrationand geo-registration information may be provided in the form ofparameters and geometric information of the video camera and videocamera field of view. The calibration information may be used forimage-based mensuration (i.e., the measurement of geometric quantities),and it may improve video understanding components, such as the targetdetector, the target tracker, or the target classifier by making, forexample, absolute position, size, speed and heading information of theobject available. The following may also improve detection of videoevents and threats by facilitating the mensuration of, for example,actual position, speed, heading, and sizes of moving targets, andestimated time of collision/impact.

With reference to FIG. 1, in an imaging or video processing system 100,a video sequence or video sequence data 110 may be obtained from a videocamera 112. Alternatively, the video sequence 110 may be prerecorded andobtained, for example, from an archive. A target detection module 120may receive the video sequence 110 and detect, track and classifytargets or events from the video sequence 110. A detector 122 may detecttargets using a background detection method, described, for example, inU.S. Pat. No. 6,999,600, identified above. Targets may be tracked usinga tracker 124, such as a Kalman filter, applied to the centroids of thetargets, or using some other technique as described, for example, inU.S. Published Patent Application No. 2005/0146605, or U.S. PublishedPatent Application No. 2006/0268111, identified above. A classifier 126may perform classification of the targets by a number of techniques.Examples of such techniques include using a neural network classifierand using a linear discriminatant classifier described, for example, in“A system for video surveillance and monitoring,” by Collins et al.,technical report CMU-RI-TR-00-12, Robotics Institute, Carnegie MellonUniversity, May, 2000. The output of the target detection module 120 istarget meta information, such as, for example, the target's width,height, and/or size within the image, location within the image, motionwithin the image, color, shape, and class type.

As described in detail below, a correspondence module 130 may determinethe correspondences between the targets detected in the video sequence110 by the target detection module 120 and corresponding targetgeo-positional data 140, which may be obtained from a geo-positionalsensor or sensors 142 associated with objects in the environment thatprovide such information. The correspondence module 130 may providecorrespondence data, which may be used to calibrate and geo-register thevideo camera 112. A camera calibration and geo-registration module 150may use the correspondences provided by the correspondence module 130 toprovide the relationship between the video camera 112 and geo-positionalsensor 142. Based on metric rules 156 and video understanding rules 158,a metric rules and video understanding module 160 may use thecalibration and geo-registration information from the calibration andgeo-registration module 150 and the target meta information from thetarget detection module 120 to analyze the video sequence 110, andgeo-locate objects in the environment including identifying,classifying, verifying the objects, and the like.

With continuing reference to FIG. 1 and further reference to FIG. 2A,the correspondence module 130 may be implemented by a technique 200. Apair of targets or objects contemporaneously observed in the videocamera data and geo-positional sensor data may be corresponding or noncorresponding objects. Thus, the simultaneous observations provide twomutually exclusive hypotheses: the observations either result from thesame object, or the observations are accidentally associated. Subsequenttopologically consistent pairs of observations support the firsthypothesis and indicate that the observed pair may be the same target orobject. Subsequent topologically inconsistent pairs of observationsstrengthen the second hypothesis, indicating that the originalassociation was accidental.

Let VC and GS be the video camera data and geo-positional sensor data,respectively, provided by respective video camera 112 and geo-positionalsensor 142. Each observation includes an observational spatial positionx=[x y]′ in video camera or geo-positional sensor coordinate system. Inblock 202, the objects which are simultaneously observed in the videocamera and geo-positional sensor data VC, GS may be obtained. Forexample, objects in the video camera data VC may be obtained from thevideo camera 112, and objects in the geo-positional sensor data GS maybe obtained from the geo-positional sensor 142. In block 204, the pairsof objects, for which the first hypothesis has substantially moresupport than the second hypothesis, may be identified based onpredetermined criteria.

In block 206, the spatial and spatiotemporal constraints for eachobserved pair may be verified. If the spatial and spatiotemporalconstraints are satisfied, in block 207, the observed pair may bediscarded. If the spatial and spatiotemporal constraints are notsatisfied, in block 208, when the observation objects pair issimultaneously recorded in the video camera and geo-positional sensordata, the observed objects pair may be added to the sample C.

The spatial constraints that may be verified in block 206 may includefor example, a point referenced in the geo-positional sensor data GSthat lies in the visibility map. The visibility map may be constructedby incrementing points on the geo-referenced map when nothing is visiblein the video camera 112. An exemplary visibility map 210 is shown inFIG. 2B. The field of view of the video camera 112 may take the form ofa trapezoid 212. The video camera 112 may be located at an intersection214 of two non-parallel sides of the trapezoid 212.

The spatiotemporal constraints that may be verified in block 206 mayinclude, for example, a point observed in the geo-positional sensor datathat may be stationary while a corresponding point observed in the videocamera-derived data may be moving; a point observed in thegeo-positional sensor data may be moving while a corresponding pointobserved in the video camera-derived data may be stationary; or atrajectory of an object observed in the geo-positional sensor data mayconform to a straight line while a trajectory observed a correspondingobject observed in the video camera may be curved.

In block 224, a filtering mechanism using, for example, a robuststatistical estimation of model parameters, such as, for example, aRandom Sample Consensus (RANSAC) algorithm, may be used on theidentified object pairs to eliminate noisy pairs and estimate thehomography that best describes the pairs. Generally, the RANSAC algoritmmay be used to estimate parameters of a mathematical model from a set ofobserved data. A basic assumption is that the data consists of inliers,i.e., data points which can be explained by some set of modelparameters, and outliers which are data points that do not fit themodel. In addition to this, the data points may be subject to noise. Theoutliers may originate, for example, from extreme values of the noise orfrom erroneous measurements or incorrect hypotheses about theinterpretation of data. Based on a set of inliers, RANSAC may estimatethe parameters of a model that optimally explains or fits the data set.

In block 226, to estimate locations of simultaneous observation pairs,the joint probable density distribution p(x₁, x₂) of spatial positionsof simultaneous observations may be determined for the video camera andgeo-positional sensor data VC, GS, where (x₁, x₂) is the orderedobservation object pair in the video camera and geo-positional sensordata VC, GS.

In block 228, the joint probability density function for simultaneousobservations may be estimated. As an example, a Parzen window method maybe used to estimate the joint probability density function. The Parzenwindow approach is a density estimation technique that, rather thanimposing assumptions on the density function, allows directapproximation of the density in a non-parametric fashion. For example,given some data regarding a sample of population, the Parzen windowmethod may be used to extrapolate the data to the entire population. Forexample, assume a sample C includes n d-dimensional data points c₁, c₂,. . . , c_(n) of a multi-variate distribution p(c). An estimate{circumflex over (p)}(c) of the joint probability density function maybe calculated as:

$\begin{matrix}{{\hat{p}(c)} = {\frac{1}{n}{B}^{{- 1}/2}{\sum\limits_{i = 1}^{n}\; {K\left( {B^{{- 1}/2}\left( {c - c_{i}} \right)} \right)}}}} & (1)\end{matrix}$

where the d-dimensional kernel K(c) is a bounded function satisfying∫K(c)dc=1, B is a symmetric d×d band-width matrix, and c is a featurevector.

The band-width matrix B may be selected empirically (for example, byapproximating band-width matrix B as a diagonal matrix) or may beestimated using different data-driven techniques as described, forexample, in “Bandwidth Selection for Multivariate Kernel DensityEstimation using MCMC,” by Zhang X., et al., Computational Statisticsand Data Analysis, 50(11), 3009-3031. Examples of kernel functions, K(c)may include a normal, Epanechnikov, uniform, or triangle kernel.

The feature vector c, used for determining the joint probability densityfunction between the video camera data and geo-positional sensor data VCand GS, i.e., p(x₁, x₂), may be a four dimensional vector, consisting ofthe x and y coordinates of simultaneous pairs observations in the videocamera and geo-positional sensor.

In block 240, once the joint probable density distribution p(x₁, x₂) ofspatial positions of simultaneous observations is estimated, densitymodes m₁, m₂, . . . , m_(k) or modes with high simultaneous density inthe video camera and geo-positional sensor data VC, GS may bedetermined, using, for example, a mean-shift based approach. Accordingto the mean-shift based approach, a density mode m_(i) may representlocation pairs (x_(a), x_(b)) in the video camera and geo-positionalsensor data VC, GS where the simultaneous density is high.

In block 250, weighting may be assigned to each density mode determinedin block 240. Because the high simultaneous density might be a result ofan intense density at one of the two locations and accidental alignmentat the other, a correlation coefficient may be applied as a weight foreach density mode. The sensor output may be modeled at each locationx_(a) as a binary random variable X_(a), where X_(a)=1, if there is anobservation at x_(a) and X_(a)=0 otherwise. A weight w(m_(i)) may beassigned to each mode m_(i)=(x_(a), x_(b)) equal to the Pearsoncorrelation coefficient between the random variables X_(a) and X_(b):

$\begin{matrix}{{w\left( m_{i} \right)} = \frac{{p\left( {X_{a},X_{b}} \right)} - {{p\left( X_{a} \right)}{p\left( X_{b} \right)}}}{{p\left( X_{a} \right)}{p\left( X_{b} \right)}\left( {1 - {p\left( X_{a} \right)}} \right)\left( {1 - {p\left( X_{b} \right)}} \right)}} & (2)\end{matrix}$

In block 260, the weighted density modes may be provided.

With continuing reference to FIG. 1 and further reference to FIG. 3, thecamera calibration and geo-registration module 150 may use a technique300. More specifically, the camera calibration and geo-registrationmodule 150 may use the weighted density modes provided by thecorrespondence module 130 to determine a relationship between the fieldof view of the video camera 112 and the field of view of thegeo-positional sensor 142. The relationship between the field of view ofthe video camera 112 and the field of view of the geo-positional sensor142 may be represented in terms of the homography given, for example, bya 3×3 matrix H between corresponding points on the ground-plane of thetwo images. If p1 is a point on the ground-plane in the video cameradata and p2 is a corresponding point (projection of the same worldlocation as p1) on the ground-plane in the geo-positional sensor data,then p1=H*p2, where the points p1, p2 are the points of a homogeneouscoordinate system. The homography H* may be obtained by solving a linearsystem determined by identifying four or more corresponding points inthe two images that lie on the ground-plane in the video camera andgeo-positional sensor data and do not form degenerate cases, such as,co-linearity.

In block 310, a number of top highly weighted density modes m₁, m₂, . .. , m_(k) in the joint probable density distribution p(x₁,x₂) of spatialpositions of simultaneous observations from the weighted density modesprovided in block 260 by the correspondence module 130 may be selected.The density modes may be selected, for example, by selecting threehighest weighted modes.

In block 320, a robust statistics algorithm, such as, for example, theRANSAC algorithm with the least median square algorithm, may be used todetermine the homography H* that best fits the selected highly weightedmodes. Such a technique is described, for example, in “Multiple viewgeometry in computer vision,” by R. Hartley and A. Zisserman, 2003, pp.107-110. The homography H* provides (a) intrinsic parameters 330 of thevideo camera 112, such as, for example, the focal length and thecoordinates of the principal point, and (b) extrinsic parameters 340 ofthe video camera 112, such as, for example, the position and field ofview. The position may include, for example, the latitude and longitudeof the video camera 112, or be relative to a satellite image or a map.In one embodiment, the video camera field of view may be specified via,for example, a geographic information system (GIS) input, such as, forexample, a satellite image, an electronic nautical chart (ENC), adigital line graph (DLG), a digital elevation map (DEM), a digitalraster graphic (DRG), a digital orthophoto quadrangle (DOQ), a geologicmap, or a geospatial information system input.

Referring back to FIG. 1, the metric rules and video understandingmodule 160 may use the metric rules and video understanding rules 156,158 and the calibration and geo-registration information from thecalibration and geo-registration module 150 and the target metainformation from the target detection module 120 to geo-locate thetargets, objects or events including target detection with a targetdetection module 170, target identification with a target identificationmodule 172, target classification with a target classification module174, target verification with a target verification module 176, andtarget tracking with a target tracking module 178. The mapping betweenthe image coordinates in the video image camera to world locations maybe specified by the determined homography H*, describing therelationship between the video camera field of view and thegeo-positional sensor field of view. The target detection module 170 maydetect targets using a background detection method, described, forexample, in U.S. Pat. No. 6,999,600, identified above. The targetidentification module 172 may identify the detected targets with theincoming geo-positional data by using the calibration andgeo-registration information. The target classification module 174 mayperform classification of the targets by a number of techniques.Examples of such techniques include using a neural network classifier orusing a linear discriminatant classifier described, for example, in “Asystem for video surveillance and monitoring,” by Collins et al.,technical report CMU-RI-TR-00-12, Robotics Institute, Carnegie MellonUniversity, May, 2000. The target verification module 176 may verifytargets by comparing i) the target types sent by the target itself(through the geo-positional data) with the target type determined by theclassification module, and ii) the metric size calculated by using thetarget detection, and calibration information with the size sent by thetarget. The target tracking module 178 may track targets using, forexample a Kalman filter, applied to the centroids of the targets, orusing some other technique as described, for example, in U.S. PublishedPatent Application No. 2005/0146605, or U.S. Published PatentApplication No. 2006/0268111, identified above.

In the target detection module 170, mapping targets detected in thevideo sequence 110 to world locations may allow detecting, based on themetric rules 156, when targets, for example: (a) move too close tospecified world locations; (b) move too close to a specified boundary;(c) move too far away from specified world locations, (d) move too faraway from a specified boundary, (e) move too close to other movingtargets, (f) move too far away from other moving targets, (g) travelfaster than a specified speed, (h) travel slower than a specified speed,(i) exceed a specified size, (j) are smaller than a specified size; and(k) other like rules. The metric rules 156 may also be used to detectcombinations of metric observations, such as, for example: (a) targetsthat move too fast for their size, given their current speed and headingand are likely to get too close to a specified world location within aspecified time; or (b) targets that move too fast given their currentspeed and heading and are likely to breach a specified boundary within aspecified time. The metric rules 156 may include, for example, the imagecentric rules described, for example, in U.S. Published PatentApplication No. 2005/0162515, identified above. The metric rules 156 andvideo understanding rules 158 may, for example, also be implemented asimage-based rules described in U.S. Published Patent Application No.2005/0162515. For example, target meta information may be automaticallycombined with calibration and geo-registration information before thejoint information is compared against rules specified by the operator ofthe system. Rule specification and matching process may, for example,also be implemented as described in U.S. Published Patent ApplicationNo. 2005/0162515.

With continuing reference to FIG. 1 and particular reference to FIG. 4,this exemplary embodiment may include port surveillance and monitoring.Large ships may typically communicate their status and identificationinformation through an automatic identification system (AIS). AIS may beused by ships and vessel traffic systems (VTS) principally foridentification of ships at sea. The IMO (International MaritimeOrganization) regulations currently require an AIS transponder to bepresent aboard each ship of 300 gross tonnage and above engaged oninternational voyages, each cargo ship of 500 gross tonnage and abovenot engaged on international voyages, and all passenger shipsirrespective of size. The AIS transponder may help to identify ships,which are not in sight (e.g., at night, in fog, in radar blind arcs orshadows or at distance), by transmitting identification (ID), position,course, speed and other ship data to all other nearby ships and VTSstations.

In an exemplary port surveillance scenario 400 of FIG. 4, the port basin402 may be monitored by a video camera 112, such as video camera 410,which may detect moving objects in a camera field of view 412. Thedetected objects may include, for example, ships, boats, moving objectson land, airplanes and other objects in the camera field of view 412. Asillustrated, the camera detected objects are designated by white boxesaround them. Other ships, which are out of the camera field of view 412,are not detected by the video camera 410. A geo-positional sensor 142,such as an AIS receiver 420, may receive information broadcasted fromeach ship or object within a range of the AIS receiver 420 from arespective AIS transponder 422 of each ship. In FIG. 4, the referencenumeral 422 and its several lead lines point to the AIS transponders inthe port basin 402. The broadcast of the ship AIS transponder 422 mayinclude information such as, for example, identification (ID), position,course, speed and other ship data. Although the AIS receiver 420 isillustrated as the only source of the geo-positional information, it iscontemplated that many other sources of the geo-positional informationmay be used.

In one embodiment, the target geo-positional data 140 may be extractedfrom the information obtained, for instance, by the AIS receiver 420 andprovided to the correspondence module 130. Based on the detections inthe video sequence 110 by the target detection module 120 and the targetgeo-positional data 140 extracted by the AIS receiver 420, the cameracalibration and geo-registration module 150 may calibrate the videocamera 112 if it is known which detections correspond to which AISreading and if enough of the correspondences are available. The numberof correspondences required depends on the complexity of theimage-to-world mapping model and thus on the number of parameters to beestimated. For example, a minimum of four such image-to-worldcorrespondence pairs may be required to compute a planar homography(e.g., mapping model) of the image from the video camera with thegeographical coordinates. In practice, the theoretical minimum number ofcorrespondences may not be enough due to errors and inaccuracies in themeasurement. More data may be required to achieve better accuracy. Morecomplex mapping models may involve multiple planar surfaces or make useof geographical information systems or digital elevation maps to mapimage locations to three-dimensional world locations.

Although the AIS receiver 420 may receive the broadcast from theAIS-equipped ships within the range, only a small portion of this rangemay be visible in the camera field of view 412. For example, in FIG. 4,five ships 430, 432, 434, 436, 438 shown at sea and two ships 450, 452shown in port are not in the field of view 412 of the video camera 410.Hence, a small number (or none in some situations) of the ships equippedwith the AIS transponders 422 and detected by the AIS receiver 420 maybe detected by the video processing system 100. In addition, much of theport-based activity may consist of small boats and recreational craftthat are not usually equipped with AIS transponders. These vehicles maygenerate a significant amount of detections in the imaging system 100.However, these detections may have no corresponding data obtained by theAIS receiver 420.

With reference to FIG. 5, this exemplary embodiment is similar to theembodiment of FIG. 1, except that the camera calibration andgeo-registration module 150 is replaced by a camera calibration andgeo-registration module 500, which uses a camera geo-positional data 520and the correspondence information from the correspondence module 130 toimprove the speed and/or accuracy of the calibration of the video camera112. The camera geo-positional data 520 and the determinedcorrespondences of target geo-positional data and targets detected inthe video sequence 110 may be used for geo-registration. The camerageo-positional data 520 may be obtained from the geo-positional sensoror sensors 142. The availability of the camera geo-positional data 520may reduce the search space of the camera calibration andgeo-registration module 500. For example, instead of estimating theposition, viewing direction and camera field of view from theinformation of the target detected in the video sequence 110 and thetarget geo-positional data 140, in this embodiment, only the viewingdirection and camera field of view need to be estimated. Additionally,the availability of the camera geo-positional data 520 may reduce theamount of relevant data as the geo-positional data from distant targetsmay be ignored. The availability of the camera geo-positional data 520may simplify the solution space and thus speed convergence or (if thenumber of correspondences is kept fixed) improve accuracy or both.

With reference to FIG. 6, this exemplary embodiment is similar to theembodiment of FIG. 5, except the metric rules and video understandingmodule 160 is replaced by a metric rules and video understanding module600. The metric rules and video understanding module 600 may use thecamera calibration and geo-registration information from the cameracalibration and geo-registration module 500 and satellite imagery 610for the visualization of targets on a satellite image. The targetgeo-positional data 140 may be fed through the correspondence and cameracalibration and geo-registration modules 130, 500 to the metric rulesand video understanding module 600 and may be used to indicate thetargets on the satellite imagery 610.

FIG. 7 illustrates another exemplary embodiment, which enables targetvisualization similarly to the embodiment of FIG. 6 but without usingthe camera geo-positional data. The embodiment of FIG. 7 is similar tothe embodiment of FIG. 1, except that the satellite imagery 610 isprovided to a metric rules and video understanding module 700.

With reference again to FIGS. 1, 4, 5, 6 and continuing reference toFIG. 7, for port surveillance and monitoring, the calibration andgeo-registration of one or more video cameras 112 by the cameracalibration and geo-registration module 150, 500 provides a number ofbenefits. The learned camera-view-to-world mapping may allow the targetverification 180 to verify the ship identities advertised by the shipAIS transponders 422. For example, the advertised ship geo-position,size, speed and heading, for example, may be compared to the shipgeo-position, size, speed and heading detected in the video sequence110. Furthermore, the camera-view-to-world mapping obtained viacalibration and geo-registration of the video cameras 410 may providegeo-positional, size, speed and heading information for the ships thatdo not transmit geo-positional data (e.g., AIS).

In addition to the described above port surveillance and monitoring, thedescribed above may be used for other applications, such as, forexample: port security; video understanding; perimeter and bordersecurity; site model building; traffic monitoring; and the like. Otherapplications will become apparent to those of ordinary skill in the art.

For video understanding, the mensuration of the targets obtained fromthe metric rules and video understanding module 160, 600, 700 may beused to improve the analysis of a video sequence. The world position,size, speed, and heading of targets provide contextual information thatmay be used to improve various components of video analysis systems. Forexample, the target detection may benefit from target geo-positionalinformation. Using the embodiments of FIG. 6 or 7, the targetgeo-positional information may indicate that a target travels on water.If this information is made available to the target detection 170, thetarget detection 170 may be tuned to improve target detection. Asanother example, the target detection 170 may benefit from target speedand heading information by sensitizing the target detection in the pathof the target. As yet another example, the target tracking 182 maybenefit from target size information to estimate the inertia of thetarget. Inertia prevents larger targets from changing direction quickly.This information may be used when the tracker must resolve targetocclusions. As yet another example, the target size may benefit thetarget classification 176. If for instance the size of a target is knownto be four meters, the target classification 176 may eliminate thefollowing target type hypotheses, for example: human, semi-trailertruck, cruise ships, cargo vessels, etc.

For security and video surveillance, the detection of video events andthreats by the metric rules and video understanding module 160, 600, 700may be improved by using, for example, actual position, speed, heading,and size of moving targets, and/or estimated time of collision orimpact.

For traffic monitoring, the calibration and geo-registration of one ormore video cameras 112 with the camera calibration and geo-registrationmodule 150, 500 provides actual target speeds, while uncalibrated videocameras may only provide image speeds and headings. Calibrated andgeo-registered video cameras may, for example, detect speeding vehicles,running people, drifting boats, oversize vehicles, wrong way traffic, orillegal access by targets that violate these properties or theircombinations.

FIG. 8 depicts another exemplary video processing system. The videoprocessing system 800 may include the video camera or cameras 112 thegeo-positional sensor or sensors 142, a communication medium 804, ananalysis system 806, a user interface 808, and a triggered response 810.The video camera 112 may be trained on a video monitored area and maygenerate output signals. In an exemplary embodiment, the video camera112 may be positioned to perform surveillance of an area of interest. Inaddition to the target geo-positional data received from thegeo-positional sensor 142, the video processing system 800, similarly tothe described above, may receive target geo-positional data from one ormore objects which transmit target geo-positional information in theenvironment, such as, for example, geographic information system (GIS)input, such as a satellite image, an electronic nautical chart (ENC), adigital line graph (DLG), a digital elevation map (DEM), a digitalraster graphic (DRG), a digital orthophoto quadrangle (DOQ), or ageologic map.

In an exemplary embodiment, the video camera 112 may be equipped to beremotely moved, adjusted, and/or controlled. With such video cameras,the communication medium 804 between the video camera 112 and theanalysis system 806 may be bi-directional, and the analysis system 806may direct the movement, adjustment, and/or control of the video camera112.

In an exemplary embodiment, the video camera 112 may include multiplevideo cameras monitoring the same video monitored area.

In an exemplary embodiment, the video camera 112 may include multiplevideo cameras monitoring multiple video monitored areas.

The communication medium 804 may transmit the output of the video camera112 and/or the geo-positional sensor 142 to the analysis system 806. Thecommunication medium 804 may be, for example: a cable; a wirelessconnection; a network (e.g., a number of computer systems and associateddevices connected by communication facilities; permanent connections(e.g., one or more cables); temporary connections (e.g., those madethrough telephone, wireless, or other communication links); an internet,such as the Internet; an intranet; a local area network (LAN); a widearea network (WAN); a combination of networks, such as an internet andan intranet); a direct connection; an indirect connection). Ifcommunication over the communication medium 804 requires modulation,coding, compression, or other communication-related signal processing,the ability to perform such signal processing may be provided as part ofthe video camera 112 and/or separately coupled to the video camera 112.The same or different communication mediums 804 may be used for any orall video cameras 112 and any or all of the geo-positional sensors 142.

The analysis system 806 may perform analysis tasks, including necessaryprocessing according to the invention. For example, the analysis system806 may perform the tasks described above for the modules 120, 130, 150,160, 500, 600, and/or 700 illustrated in FIGS. 1, 5, 6, 7. The analysissystem 806 may include a receiver 820, a computer system 822, and acomputer-readable medium 824.

The receiver 820 may receive the output signals of the video camera orcameras 112 and geo-positional sensor or sensors 142 via thecommunication medium 804. If the output signals of the video camera 112and/or geo-positional sensor 142 have been modulated, coded, compressed,or otherwise communication-related signal processed, the receiver 820may be able to perform demodulation, decoding, decompression or othercommunication-related signal processing to obtain the output signalsfrom the video camera 112 and/or geo-positional sensor 142, orvariations thereof due to any signal processing. Furthermore, if thesignals received from the communication medium 804 are in analog form,the receiver 820 may be able to convert the analog signals into digitalsignals suitable for processing by the computer system 822. The receiver820 may be implemented as a separate block (shown) and/or integratedinto the computer system 822. Also, if it is unnecessary to perform anysignal processing prior to sending the signals via the communicationmedium 804 to the computer system 822, the receiver 820 may be omitted.

The computer system 822 may be coupled to the receiver 820, thecomputer-readable medium 824, the user interface 808, and the triggeredresponse 810. The computer system 822 may perform analysis tasks,including necessary processing according to the invention.

The computer-readable medium 824 may include all necessary memoryresources required by the computer system 822 for the invention and mayalso include one or more recording devices for storing signals receivedfrom the communication medium 804 and/or other sources. Thecomputer-readable medium 824 may be external to the computer system 822(shown) and/or internal to the computer system 822.

The user interface 808 may provide input to and may receive output fromthe analysis system 806. The user interface 808 may include, forexample, one or more of the following: a monitor; a mouse; a keyboard; akeypad; a touch screen; a printer; speakers and/or one or more otherinput and/or output devices. The user interface 808, or a portionthereof, may be wirelessly coupled to the analysis system 806. Usinguser interface 808, a user may provide inputs to the analysis system806, including those needed to initialize the analysis system 106,provide input to analysis system 806, and receive output from theanalysis system 806.

The triggered response 810 may include one or more responses triggeredby the analysis system. The triggered response 810, or a portionthereof, may be wirelessly coupled to the analysis system 806. Examplesof the triggered response 810 include: initiating an alarm (e.g., audio,visual, and/or mechanical); sending a wireless signal; controlling anaudible alarm system (e.g., to notify the target, security personneland/or law enforcement personnel); controlling a silent alarm system(e.g., to notify security personnel and/or law enforcement personnel);accessing an alerting device or system (e.g., pager, telephone, e-mail,and/or a personal digital assistant (PDA)); sending an alert (e.g.,containing imagery of the violator, time, location, etc.) to a guard orother interested party; logging alert data to a database; taking asnapshot using the video camera 112 or another camera; culling asnapshot from the video obtained by the video camera 112; recordingvideo with a video recording device (e.g., an analog or digital videorecorder); controlling a PTZ camera to zoom in to the target;controlling a PTZ camera to automatically track the target; performingrecognition of the target using, for example, biometric technologies ormanual inspection; closing one or more doors to physically prevent atarget from reaching an intended target and/or preventing the targetfrom escaping; controlling an access control system to automaticallylock, unlock, open, and/or close portals in response to an event; orother responses.

In an exemplary embodiment, the analysis system 806 may be part of thevideo camera 112. For this embodiment, the communication medium 804 andthe receiver 820 may be omitted. The computer system 822 may beimplemented with application-specific hardware, such as a DSP, a FPGA, achip, chips, or a chip set to perform the invention. The user interface808 may be part of the video camera 112 and/or coupled to the videocamera 112. As an option, the user interface 808 may be coupled to thecomputer system 822 during installation or manufacture, removedthereafter, and not used during use of the video camera 112. Thetriggered response 880 may be part of the video camera 112 and/orcoupled to the video camera 112.

In an exemplary embodiment, the analysis system 806 may be part of anapparatus, such as the video camera 112 as discussed in the previousparagraph, or a different apparatus, such as a digital video recorder ora router. For this embodiment, the communication medium 804 and thereceiver 820 may be omitted. The computer system 822 may be implementedwith application-specific hardware, such as a DSP, a FPGA, a chip,chips, or a chip set to perform the invention. The user interface 808may be part of the apparatus and/or coupled to the apparatus. As anoption, the user interface 808 may be coupled to the computer system 822during installation or manufacture, removed thereafter, and not usedduring use of the apparatus. The triggered response 880 may be part ofthe apparatus and/or coupled to the apparatus.

The above has been described in detail with respect to the exemplaryembodiments, and it will now be apparent from the foregoing to thoseskilled in the art that changes and modifications may be made withoutdeparting from the described in its broader aspects. The invention,therefore, as defined in the appended claims, is intended to cover allsuch changes and modifications as fall within the true spirit of theinvention.

1. A computer-readable medium comprising software for video processing,which when executed by a computer system, cause the computer system toperform operations comprising a method of: receiving a video sequence ofa field of view within an environment; detecting targets in the videosequence; receiving target geo-positional information transmitted by ageo-positional sensor; and determining correspondences between thetargets detected in the video sequence and the target geo-positionalinformation.
 2. The computer-readable medium as set forth in claim 1,wherein the method further comprises: receiving geo-positionalinformation regarding a video camera that generated the video sequence,wherein the correspondences are further determined based on thegeo-positional information of the video camera that generated the videosequence.
 3. The computer-readable medium as set forth in claim 1,wherein the method further comprises: receiving geo-positional targetinformation from a global positioning system (GPS) transmitter.
 4. Thecomputer-readable medium as set forth in claim 1, wherein the methodfurther comprises: receiving geo-positional target information from anautomatic identification system (AIS) transmitter.
 5. Thecomputer-readable medium as set forth in claim 1, wherein the methodfurther comprises: receiving geo-positional target information from ablue force tracker (BFT) transmitter.
 6. The computer-readable medium asset forth in claim 1, wherein the method further comprises: receivinggeo-positional target information from a cell phone.
 7. Thecomputer-readable medium as set forth in claim 1, wherein the methodfurther comprises: receiving geo-positional target information from aradio frequency identification (RFID) tag.
 8. The computer-readablemedium as set forth in claim 1, wherein the method further comprises:estimating at least one intrinsic parameter or at least one extrinsicparameter of a video camera that generated the video sequence based onthe determined correspondences.
 9. The computer-readable medium as setforth in claim 1, wherein the method further comprises: geo-registeringthe field of view of a video camera that generated the video sequencebased at least on the determined correspondences.
 10. Thecomputer-readable medium as set forth in claim 1, wherein the methodfurther comprises: geo-registering the field of view of a video camerathat generated the video sequence based on geographic imaging systemimagery of the environment.
 11. The computer-readable medium as setforth in claim 10, wherein the geographic imaging system imagerycomprises satellite imagery.
 12. The computer-readable medium as setforth in claim 10, wherein the geographic imaging system imagerycomprises nautical charts.
 13. The computer-readable medium as set forthin claim 10, wherein the geographic imaging system imagery compriseselectronic nautical charts.
 14. The computer-readable medium as setforth in claim 10, wherein the geographic imaging system imagerycomprises Digital Elevation Maps.
 15. The computer-readable medium asset forth in claim 10, wherein the geographic imaging system imagerycomprises a geospatial information system input.
 16. Thecomputer-readable medium as set forth in claim 1, wherein the methodfurther comprises: determining a measurement of at least one of thetarget or the environment based on the determined correspondences. 17.The computer-readable medium as set forth in claim 1, whereindetermining the correspondences comprises: determining correspondingpairs of targets detected in the video sequence and received targetgeo-positional information based on a predetermined criteria;determining a probability density function of joint distribution ofspatial positions of each determined corresponding pair; and estimatinghigh joint density modes.
 18. The computer-readable medium as set forthin claim 17, wherein the method further comprises: weighting the densitymodes with correlation coefficients; selecting a predetermined number ofhigher weighted density modes; and determining homography for theselected higher weighted density modes.
 19. The computer-readable mediumas set forth in claim 1, wherein the method further comprises: based onthe determined correspondences, at least one of: detecting a target,identifying a target, classifying a target, verifying a target, ortracking a target.
 20. The computer-readable medium as set forth inclaim 1, wherein the method further comprises: comparing the targetgeo-positional information to meta information of a corresponding targetdetected in the video sequence to obtain a comparison result; and atleast one of identifying or verifying at least one of the targetdetected in the video sequence or target geo-positional informationbased on the comparison result.
 21. A computer-based system to perform amethod for video processing, the method comprising: receiving a videosequence of a field of view within an environment; detecting targets inthe video sequence; receiving target geo-positional informationtransmitted by a geo-positional sensor; and determining correspondencesbetween the targets detected in the video sequence and the targetgeo-positional information.
 22. The system as set forth in claim 21,wherein the method further comprises: receiving geo-positionalinformation regarding a video camera that generated the video sequence,wherein the correspondences are further determined based on thegeo-positional information of the video camera that generated the videosequence.
 23. The system as set forth in claim 21, wherein the methodfurther comprises: estimating at least one intrinsic, or at least oneextrinsic parameter of a video camera that generated the video sequencebased on the determined correspondences.
 24. The system as set forth inclaim 21, wherein the method further comprises: geo-registering thefield of view of a video camera that generated the video sequence basedat least on the determined correspondences.
 25. The system as set forthin claim 21, further comprising: geo-registering the field of view of avideo camera that generated the video sequence based on geographicimaging system imagery of the environment.
 26. The system as set forthin claim 25, wherein the geographic imaging system imagery comprisessatellite imagery.
 27. The system as set forth in claim 21, wherein themethod further comprises: determining measurements of at least one ofthe target or the environment based on the determined correspondences.28. The system as set forth in claim 21, wherein determining thecorrespondences comprises: determining corresponding pairs of targetsdetected in the video sequence and received target geo-positionalinformation based on a predetermined criteria; determining a probabilitydensity function of joint distribution of spatial positions of eachdetermined corresponding pair; and estimating high joint density modes.29. The system as set forth in claim 28, wherein the method furthercomprises: weighting the density modes with correlation coefficients;selecting a predetermined number of higher weighted density modes; anddetermining homography for the selected higher weighted density modes.30. The system as set forth in claim 21, wherein method furthercomprises: based on the determined the determined correspondences, atleast one of: detecting a target, identifying a target, classifying atarget, verifying a target, or tracking a target.
 31. The system as setforth in claim 21, wherein the method further comprises: comparing thetarget geo-positional information to meta information of a correspondingtarget detected in the video sequence to obtain a comparison result; andat least one of identifying or verifying at least one of the targetdetected in the video sequence or target geo-positional informationbased on the comparison result.
 32. A method for video processing,comprising: receiving a video sequence of a field of view within anenvironment; detecting targets in the video sequence; receiving targetgeo-positional information transmitted by a geo-positional sensor; anddetermining correspondences between the targets detected in the videosequence and the target geo-positional information.
 33. The method asset forth in claim 32, further comprising: receiving geo-positionalinformation regarding a video camera that generated the video sequence,wherein the correspondences are further determined based on thegeo-positional information of the video camera that generated the videosequence.
 34. The method as set forth in claim 32, further comprising:estimating at least one intrinsic parameter, or at least one extrinsicparameter of a video camera that generated the video sequence based onthe determined correspondences.
 35. The method as set forth in claim 32,further comprising: geo-registering the field of view of a video camerathat generated the video sequence based at least on the determinedcorrespondences.
 36. The method as set forth in claim 32, furthercomprising: geo-registering the field of view of a video camera thatgenerated the video sequence based on geographic imaging system imageryof the environment.
 37. The method as set forth in claim 36, wherein thegeographic imaging system imagery comprises satellite imagery.
 38. Themethod as set forth in claim 32, further comprising: determining ameasurement of at least one of the target or the environment based onthe determined correspondences.
 39. The method as set forth in claim 32,wherein determining the correspondences comprises: determiningcorresponding pairs of targets detected in the video sequence andreceived target geo-positional information based on a predeterminedcriteria; determining a probability density function of jointdistribution of spatial positions of each determined corresponding pair;and estimating high joint density modes.
 40. The method as set forth inclaim 39, further comprising: weighting the density modes withcorrelation coefficients; selecting a predetermined number of higherweighted modes; and determining homography for the selected higherweighted modes.
 41. The method as set forth in claim 32, furthercomprising: based on the determined the determined correspondences, atleast one of: detecting a target, identifying a target, classifying atarget, verifying a target, or tracking a target.
 42. The method as setforth in claim 32, further comprising: comparing the targetgeo-positional information and meta information of a correspondingtarget detected in the video sequence to obtain a comparison result; andat least one of identifying or verifying at least one of the targetdetected in the video sequence or target geo-positional informationbased on the comparison result.
 43. An apparatus to perform the videoprocessing method of claim
 32. 44. A system to perform video processingof a video sequence generated by at least one video camera having afield of view within an environment, comprising at least one of: acorrespondence module to determine correspondences between targetsdetected in the video sequence and corresponding target geo-positionalinformation transmitted by a geo-positional sensor; a calibration andgeo-registration module to determine a relationship between the videocamera and geo-positional sensor and geo-register the video camera basedat least on the determined correspondences; or a metric rules and videounderstanding module to obtain measurement information regarding atleast one of the environment or target detected in the video sequence.45. The system as set forth in claim 44, comprising at least two of: thecorrespondence module to determine correspondences between the targetsdetected in the video sequence and corresponding target geo-positionalinformation transmitted by the geo-positional sensor; the calibrationand geo-registration module to determine the relationship between thevideo camera and geo-positional sensor and geo-register the video camerabased at least on the determined correspondences; or the metric rulesand video understanding module to obtain measurement informationregarding at least one of the environment or target detected in thevideo sequence.